Chaos-mutation-based Particle Swarm Optimizer for dynamic environment

The paper presents a modified particle swarm optimization (PSO) for the dynamic environment. The modified method provides a detected position for each particle, and applies the detected positions of some randomly sampled particles in the swarm to detect the dynamic change of the environment. If the environment has been detected to change, chaos mutation technology will be introduced to respond to the change in time. After that, an improved chaos mutation guided by swarm diversity has been developed to improve the responding efficiency. The proposed method has been applied to the dynamic environment constructed by the parabola function. The simulation results show the improved PSO can detect changes more accurately and respond to the changes of the environment more quickly, and has been a robust technique for the dynamic optimization.

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